Category: Recovery

>Sleep and teenagers


Sleep (or lack of…) is an interesting topic. Quality of sleep has been shown to negatively affect human performance for various reasons. Furthermore, there have been numerous reports suggesting a link between lack of sleep and depression.

The relationship between short sleep duration and depression has been suggested to be bidirectional,1 with chronic partial sleep deprivation being a potential risk factor for depression. Cross-sectional studies have found relationships between inadequate sleep and depression in adolescents,2,3 and a longitudinal study has shown that getting less sleep over time increased the symptoms of depression among middle school students.4 Short sleep duration has also been shown to be associated with suicidal ideation5 and suicidal behavior6 in adolescents and adults7 in cross-sectional studies.

A recent quasi-experimental study conducted by Gangwish et al. (2010) has looked at the relationships between parental set bedtimes, sleep duration, and depression in adolescents to explore the potentially bidirectional relationship between short sleep duration and depression.

For this scope they analysed 15,659 US adolescents in grades 7 to 12. The results showed that adolescents with parental set bedtimes of midnight or later were 24% more likely to suffer from depression (OR = 1.24, 95% CI 1.04-1.49) and 20% more likely to have suicidal ideation (1.20, 1.01-1.41) than adolescents with parental set bedtimes of 10:00 PM or earlier, after controlling for covariates. Consistent with sleep duration and perception of getting enough sleep acting as mediators, the inclusion of these variables in the multivariate models appreciably attenuated the associations for depression (1.07, 0.88-1.30) and suicidal ideation (1.09, 0.92-1.29).

From Table 3
Odds ratios (95% CI) for depression

Model 1a Model 2b Model 3c Model 4d
Parental set bedtime on weekday nights
10:00 PM or earlier 1.00 1.00 1.00 1.00
By 11:00 PM 1.15 (0.94-1.40) 1.13 (0.90-1.42) 1.10 (0.87-1.39) 0.97 (0.76-1.24)
By or after midnight 1.42 (1.21-1.67) 1.28 (1.07-1.52) 1.24 (1.04-1.49) 1.07 (0.88-1.30)
Self-perception of how much parents care
1 – Not at all 6.82 (3.11-14.98) 5.88 (2.79-12.40)
2 – Very little 8.32 (4.58-15.12) 6.73 (3.49-12.98)
3 – Somewhat 5.50 (3.72-8.13) 4.93 (3.32-7.30)
4 – Quite a bit 2.43 (1.89-3.13) 2.16 (1.69-2.76)
5 – Very much 1.00 1.00
Adolescent-reported sleep duration
≤ 5 h 1.71 (1.22-2.39)
6 h 1.29 (0.97-1.70)
7 h 1.19 (0.96-1.48)
8 h 1.00
9 h 1.17 (0.88-1.56)
≥ 10 h 1.34 (0.95-1.89)
Enough Sleep 0.35 (0.28-0.43)
aModel 1 – Unadjusted.
bModel 2 – Adjusted for age, sex, race/ethnicity, parent’s marital status, and family receipt of public assistance.
cModel 3 – Adjusted for variables in Model 2 plus self perception of how much parents care.
dModel 4 – Adjusted for variables in Model 3 plus adolescent reported sleep duration and perception of getting enough sleep.

The results from this study provide new evidence to support the notion that short sleep duration could play a role in the etiology of depression. Earlier bedtimes could therefore be protective against adolescent depression and suicidal ideation by lengthening sleep duration.

Young athletes have to cope nowadays with various stresses, not only performance related. Studying, maintaining social contacts, training, family and peer pressure are all parts of young athlete’s  lives. Sleep is a simple thing that can make sure they recover properly and can cope with everything they have to deal with.

So, are we making sure they get good quality and good amounts of sleep?

Do we advice them on appropriate bed time?

Do we make sure they don’t spend the night playing videogames or chatting on social networks?

Do we create the right sleeping environment and routines?

Do we know if they are sleeping well?

How about a checklist?

Read Atul Gawande’s book about checklists, The Checklist Manifesto. Not only is the book loaded with fascinating stories, but it honestly changed the way I think about the world. The book’s main point is simple: no matter how expert you may be, well-designed check lists can improve outcomes. So, let’s make sure our young athletes tick all the boxes when it comes to sleep.

Monitoring training load: Quo vadis? #3

The first two posts dealt with inexpensive and more expensive methods. I will now discuss the use of psychometric tools to get another dimension of monitoring training loads. I have not discussed the use of GPS or similar technologies, but will cover this in the next post.

I really want to present some info on various tools currently used and discuss pros and cons of them.

Profile of Mood States (POMS)

The Profile of Mood States (POMS) is a psychological rating scale used to assess transient, distinct mood states. The original scale, developed by McNair et al, has 65 items describing feelings people have.  There is a brief version,  comprising 11 of the original POMS items, developed by Cella et al, in 1987.  However, this version (Brief POMS) provides only one score for overall psychological distress.  There is yet another version called the short form of the Profile of Mood States (POMS-SF) developed by Shacham in 1983.  The short form version contains 37 items, selected from the original POMS.  It retains the six subscale information provided by POMS. The POM–Bipolar is the newest addition to the POMS. It measures moods and feelings primarily in clinical rather than nonclinical settings. It can help to determine an individual’s psychiatric status for therapy, or be used to compare mood profiles associated with various personality disorders. In nonclinical settings, the POMS–Bipolar can assess mood changes produced by techniques such as psychotherapy or meditation.

Here it is possible to download a POMS scale.

This scale has been used in a variety of populations with more than 2000 studies being performed using it. However there is a paucity of data on athletes and its links to other measures of overtraining and overreaching.

The POMS assessments are self-report inventories in which respondents rate a series of mood states (such as "Untroubled" or "Sorry for things done") based on how well each item describes the respondent’s mood during one of three time frames (i.e., during the past week, including today; right now; other). Normative data are based on the "during the past week, including today" time frame. The POMS Standard form contains 65 items and takes approximately 10 minutes to complete. The respondent rates each item on a 5-point scale ranging from “Not at all” to “Extremely”. The POMS Brief form, which is ideal for use with patients for whom ordinary tasks can be difficult and time-consuming, uses the same scale as the POMS Standard form, but contains only 30 items. It takes only 5 minutes to complete. Both the POMS Standard and POMS

Brief assessments measure six identified mood factors:

• Tension-Anxiety
• Depression-Dejection
• Anger-Hostility
• Vigor-Activity
• Fatigue-Inertia
• Confusion-Bewilderment

The POMS-Bi form contains 72 items and uses a 4-point scale. It takes approximately 10 minutes to complete. Responses for the POMS-Bi range from “Much unlike this” to “Much like this”. Unlike the other POMS assessments, the POMS-Bi measures both positive and negative affects. For each of the six bipolar scales, one pole represents the positive aspects of the dimension while the other pole refers to the negative aspects:

• Composed-Anxious
• Agreeable-Hostile
• Elated-Depressed
• Confident-Unsure
• Energetic-Tired
• Clearheaded-Confused

Since 1971, numerous research studies have provided evidence for the predictive and construct validity of the POMS Standard and POMS Brief assessments. Alpha coefficient and other studies have found the POMS Standard and POMS Brief to exhibit a highly satisfactory level of internal consistency, while product moment correlations indicate a reasonable level of test-retest reliability. Factor analytic replications provide evidence of the factorial validity of the 6 mood factors, and an examination of the individual items defining each mood state supporting the content validity of the factor scores. Studies have also supported the bipolar nature of moods measured by the POMS-Bi assessment, and reliability studies have shown that POMS-Bi items demonstrate sufficient internal consistency.

One of the first encouraging studies by O’Connor et al. (1989) examined POMS scores and resting salivary cortisol levels in 14 female college swimmers during progressive increases and decreases in training volume, and were compared to the same measures in eight active college women who served as controls. Training volume increased from 2,000 yards/day in September (baseline) to a peak of 12,000 yards/day in January (overtraining), followed by a reduction in training (taper) to 4,500 yards/day by February. The swimmers experienced significant alterations in tension, depression, anger, vigor, fatigue and global mood across the training season compared to the controls. Salivary cortisol was significantly greater in the swimmers compared to the controls during baseline and overtraining, but was not different between the groups following the taper. Salivary cortisol was significantly correlated with depressed mood during overtraining (r = .50) but not at baseline or taper. Global mood, depression, and salivary cortisol were significantly higher during the overtraining phase in those swimmers classified as stale, compared to those swimmers who did not exhibit large performance decrements.

This was one of the initial studies suggesting a link between increasing training workloads, POMS scores and cortisol responses advocating the possibility of using this psychometric tool to understand how athletes were coping with training loads.

Urhausen et al. (1998) found that the parameters of mood state at rest as well as the subjective rating of perceived exertion during exercise were significantly impaired during overtraining in a follow up study with endurance athletes.

Filaire et al. (2001) used POMS together with endocrine markers to study soccer players and found that in such group decreased testosterone to cortisol ratio does not automatically lead to a decrease in team performance or a state of team overtraining. However, they suggested that combined psychological and physiological changes during high-intensity training are primarily of interest when monitoring training stress in relation to performance.

It seems therefore clear that POMS has the potential to be used to assess how athletes cope with training loads and POMS score can potentially have a link with hormonal  imbalances.

REST Q Questionnaire

The Recovery-Stress Questionnaire for Athletes [RESTQ-Sport] is a questionnaire reported to identify the extent to which athletes are physically or mentally stressed and their current perception of recovery (Kellmann & Kallus, 2000 and Kellmann & Kallus 2001). It has been used by many individuals and organizations throughout the world and can therefore be reasonably estimated to have been used on at least several thousand high-performance athletes as a diagnostic tool to detect under-recovery states and to plan recovery practices. The predecessor of this psychometric tool was a General Recovery-Stress Questionnaire (Kallus, 1995) formulated on the idea that people will respond differently to physiological and psychological demands depending on how well-rested they are when faced with these demands.

The RESTQ-Sport was constructed based on the notion that an athlete well recovered may perform better than one who is under-recovered. However, theoretical and practical concerns governed the methods used to determine the 19 subscales of the RESTQ-Sport (Kellmann & Kallus, 2000 and Kellmann & Kallus, 2001) used an a priori method of identifying each of the subscales, combining to form several scales that reflect various aspects of stress and recovery. The RESTQ-Sport was developed through research in the area of stress for the General Scale, and the Sport Scale was comprised of items observed to coincide with stress or recovery states in athletes (Kellmann & Kallus, 2001).

The test consists of 7 stress scales, and 5 recovery scales.

The scales are:

General stress
Emotional stress
Social stress
Lack of energy
Physical complaints
Social recovery
Physical recovery
General well-being
Sleep quality
Disturbed breaks
Burnout/emotional exhaustion
Fitness/being in shape
Burnout/personal accomplishment

If you are interested in knowing more about this test and have a software to score the results, I strongly suggest you buy Dr. Kellmann’s and Kallus’ book at Human Kinetics. The book also contains a software to score the questionnaire and provide you with a graph.

The graph normally looks like this one presented by James Marshall in his blog:

Figure 1

However, you can develop your own spreadsheet to score it and graph it as I did.


Many studies have shown how valid and reliable this test is. However one of the most interesting ones was published by Jurimae et al. (2004). They studied the effects of increasing training loads in competitive rowers and found significant relationships between training volume and Fatigue scores (r=0.49), Somatic Complaints (r=0.50} and Sleep Quality (r=-0.58) at the end of heavy training. In addition, significant relationships were also observed between cortisol and Fatigue scores (r=0.48) at the end of heavy training as well as between changes in cortisol and changes in Fatigue (r=0.57) and Social Stress (r=0.51).

It should be pointed out that this test cannot be performed every day as it asks the athlete about how often the respondent participated in various activities during the preceding three days/nights. A Likert-type scale is used with values ranging from 0 (never) to 6 (always) to rank the frequency of activities/experiences of the preceding 3 days/nights.

BORG scale and perception of effort

The concept of perceived exertion was introduced half a century ago and an operational definition presented with methods to measure different aspects of perceived effort, strain and fatigue. One very common method is the RPE-Scale for "Ratings of Perceived Exertion" ("the Borg Scale") officially known now as the "Borg RPE Scale®".

As Professor Borg explains: “Stevens’ "Ratio (R) scaling methods for determinations of S-R-functions have been improved in order not only to obtain relative functions but also direct ("absolute") levels of intensity. This was done by placing verbal anchors, from simple category (C) scales (rank order scales) such as "very weak", "moderate", "strong" etc at the best possible position on a ratio scale, a "CR-scale", covering the total subjective dynamic range, so that a congruence in meaning was obtained between the numbers and the anchors”.

If you are really interested in this you should read Dr. Elisabet Borg’s thesis here where she presents the innovative approach to develop the "Borg CR100 Scale®" (also called the "centiMax Scale"). I had the pleasure to listen to her lecture last year in Italy and I was impressed by the quality of work she has done to follow up her father’s intuitions on the original rate of perceived exertion.

You can read more about Dr. Elisabet Borg here and about Professor Gunnar Borg here.

Recommendations to use a "Borg Scale" is given by many professional societies, e.g. American Heart Association, American Thoracic Society, American College of Sports Medicine, British Association for Cardiac Rehabilitation

These scales can be obtained from the firm: "Borg Perception", Gunnar Borg, Rädisvägen 124, 165 73 Hässelby, Sweden. Phone 46-8-271426.

Other alternatives

There are various tools out there these days such as the following ones:

  • Life Stress (LESCA)
  • State trait anxiety inventory (STAI)
  • Athletic coping skills inventory (ACSI)

however I have no experience in using them…maybe some of you readers know more and what to write comments about any of them?

Enough info now for psychometric tools…next post will cover aspects connected to strength, power and speed.

Caffeine and carbohydrate coingested can speed up muscle glycogen resynthesis

A very interesting paper from Prof. John Hawley’s lab was recently published on the Journal of Applied Physiology suggesting that large amounts of Caffeine (8mg/kg of body mass) together with CHO can help in replenishing glycogen stores in well trained individuals after exhaustive exercise.

The abstract is here:

J Appl Physiol (May 8, 2008). doi:10.1152/japplphysiol.01121.2007
This Article
Submitted on October 18, 2007
Accepted on April 30, 2008

David J Pedersen1, Sarah J Lessard2, Vernon G Coffey3, Emmanuel G Churchley4, Andrew M Wootton4, They Ng5, Matthew J Watt6, and John A. Hawley7*
1 Diabetes and Obesity, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
2 School of Medical Sciences, RMIT University, Bundoora, Victoria, Australia
3 exercise Metabolism Group, RMIT University, Melbourne, Victoria, Australia
4 Melbourne, Victoria, Australia; School of Medical Sciences, RMIT University, Melbourne, Victoria, Australia
5 School of Medical Sciences, RMIT University, Melbourne, Victoria, Australia
6 Protein Chemistry and Metabolism, St. Vincent’s Institute of Medical Research, Fitzroy, Victoria, Australia
7 Exercise Metabolism Group, School of Medical Sciences, RMIT University, Bundoora, Victoria, Australia

We determined the effects of the co-ingestion of caffeine with carbohydrate on rates of muscle glycogen resynthesis during recovery from exhaustive exercise in 7 trained subjects who completed 2 experimental trials in a randomized, double-blind crossover design. Prior to an experiment subjects performed exhaustive cycling and consumed a low-carbohydrate diet. The following morning subjects reported to the lab and rode until volitional fatigue. Upon completion of this ride subjects consumed either carbohydrate (CHO; 4 BM-1) or carbohydrate plus caffeine (CAFF, 8 BM-1) during 4 h of passive recovery. Muscle and blood samples were taken throughout recovery. Muscle glycogen levels were similar at exhaustion and increased by a similar amount after 1 h of recovery. After 4 h of recovery CAFF resulted in higher glycogen accumulation (313 ± 69 vs. 234 ± 50 mmol±kg- d.w, P<0.001). The overall rate of resynthesis for the 4 h recovery period was 66% higher in CAFF compared to CHO (57.7 ± 18.5 vs. 38.0 ± 7.7 mmol±kg-1 d.w.h-1, P < 0.05). Phosphorylation of CAMKThr286 was similar post-exercise and after 1 h of recovery but after 4 h CAMKThr286 phosphorylation was higher in CAFF than CHO (P<0.05). Phosphorylation of AMPKThr172 and AktSer473 was similar for both treatments at all time points. We provide the first evidence that in trained subjects, the coingestion of large amounts of caffeine with carbohydrate has an additive effect on rates on post-exercise muscle glycogen accumulation compared to when carbohydrate alone is consumed.

Very interesting, however, such a high dose of caffeine might make sleep a bit difficult!